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A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques

In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real...

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Detalles Bibliográficos
Autores principales: Campanella, Sara, Altaleb, Ayham, Belli, Alberto, Pierleoni, Paola, Palma, Lorenzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098696/
https://www.ncbi.nlm.nih.gov/pubmed/37050625
http://dx.doi.org/10.3390/s23073565
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author Campanella, Sara
Altaleb, Ayham
Belli, Alberto
Pierleoni, Paola
Palma, Lorenzo
author_facet Campanella, Sara
Altaleb, Ayham
Belli, Alberto
Pierleoni, Paola
Palma, Lorenzo
author_sort Campanella, Sara
collection PubMed
description In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Based on our protocol for data pre-processing, this study proposes to analyze signals obtained from the Empatica E4 bracelet using machine-learning algorithms (Random Forest, SVM, and Logistic Regression) to determine the efficacy of the abovementioned techniques in differentiating between stressful and non-stressful situations. Photoplethysmographic and electrodermal activity signals were collected from 29 subjects to extract 27 features which were then fed into three different machine-learning algorithms for binary classification. Using MATLAB after applying the chi-square test and Pearson’s correlation coefficient on WEKA for features’ importance ranking, the results demonstrated that the Random Forest model has the highest stability (accuracy of 76.5%) using all the features. Moreover, the Random Forest applying the chi-test for feature selection reached consistent results in terms of stress evaluation based on precision, recall, and F1-measure (71%, 60%, 65%, respectively).
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spelling pubmed-100986962023-04-14 A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques Campanella, Sara Altaleb, Ayham Belli, Alberto Pierleoni, Paola Palma, Lorenzo Sensors (Basel) Article In response to challenging circumstances, the human body can experience marked levels of anxiety and distress. To prevent stress-related complications, timely identification of stress symptoms is crucial, necessitating the need for continuous stress monitoring. Wearable devices offer a means of real-time and ongoing data collection, facilitating personalized stress monitoring. Based on our protocol for data pre-processing, this study proposes to analyze signals obtained from the Empatica E4 bracelet using machine-learning algorithms (Random Forest, SVM, and Logistic Regression) to determine the efficacy of the abovementioned techniques in differentiating between stressful and non-stressful situations. Photoplethysmographic and electrodermal activity signals were collected from 29 subjects to extract 27 features which were then fed into three different machine-learning algorithms for binary classification. Using MATLAB after applying the chi-square test and Pearson’s correlation coefficient on WEKA for features’ importance ranking, the results demonstrated that the Random Forest model has the highest stability (accuracy of 76.5%) using all the features. Moreover, the Random Forest applying the chi-test for feature selection reached consistent results in terms of stress evaluation based on precision, recall, and F1-measure (71%, 60%, 65%, respectively). MDPI 2023-03-29 /pmc/articles/PMC10098696/ /pubmed/37050625 http://dx.doi.org/10.3390/s23073565 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Campanella, Sara
Altaleb, Ayham
Belli, Alberto
Pierleoni, Paola
Palma, Lorenzo
A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques
title A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques
title_full A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques
title_fullStr A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques
title_full_unstemmed A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques
title_short A Method for Stress Detection Using Empatica E4 Bracelet and Machine-Learning Techniques
title_sort method for stress detection using empatica e4 bracelet and machine-learning techniques
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10098696/
https://www.ncbi.nlm.nih.gov/pubmed/37050625
http://dx.doi.org/10.3390/s23073565
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